Data processing method and device, computer device, and storage medium

Abstract

The application belongs to the technical field of data processing, and relates to a data processing method, comprising the following steps: receiving a data processing request carrying a request parameter sent by a user through a target interface; querying project record data of a target project related to the request parameter from a database; performing risk rating on the project record data to obtain a target project risk rating result; comprehensively evaluating the risk rating result to obtain a comprehensive risk level; performing dynamic change trend analysis on the comprehensive risk level based on the project record data to obtain trend data; performing graph construction processing on the risk rating result to obtain graph data; and performing data integration and encapsulation processing on the project record data, the risk rating result, the comprehensive risk level, the trend data and the graph data to obtain response data, and rendering the response data to a display area in the target interface. The application can be applied to data display business scenarios in the fields of financial technology and medical health, and effectively improves the intelligence of data display.

Description

Technical Field

[0001] This application relates to the field of data processing technology and can be applied to fields such as financial technology and healthcare, and in particular to data processing methods, devices, computer equipment and storage media. Background Technology

[0002] Current project management systems often present project risk data in a simplistic way, typically offering only static risk levels. This lack of depth prevents multi-dimensional analysis of project risks, resulting in low intelligence in data presentation. Specifically, most existing systems rely on a single indicator for basic risk classification. This static and limited approach fails to provide adequate decision support for managers, thus reducing the sophistication of project management.

[0003] For example, in product development projects in the financial and insurance sector, traditional systems typically only display a fixed risk level label. Managers cannot determine from this label which indicators led to that level, nor can they compare the risk differences of different projects across various dimensions. Similarly, in new drug clinical trial projects in the healthcare sector, traditional methods often only provide a general risk rating. Managers struggle to identify the project's risk performance in specific dimensions such as investment returns and operational stability, making it impossible to precisely intervene in weak areas.

[0004] Therefore, there is an urgent need to provide an intelligent method for project risk assessment and presentation to improve the comprehensiveness and intelligence of risk assessment and presentation, and to assist managers in making more accurate decisions. Summary of the Invention

[0005] The purpose of this application is to provide a data processing method, apparatus, computer equipment, and storage medium to solve the technical problem that existing methods of displaying project risk data have low intelligence.

[0006] Firstly, a data processing method is provided, including: Receive data processing requests sent by users through the target interface; wherein, the data processing requests carry request parameters; Extract the request parameters from the data processing request, and query the project record data of the target project related to the request parameters from the preset database; Based on a preset multi-dimensional risk rating strategy, the project record data is processed for risk rating to obtain a risk rating result corresponding to the target project. The risk rating results are comprehensively evaluated to obtain the corresponding comprehensive risk level; Based on the project record data, a dynamic trend analysis of the comprehensive risk level is performed to obtain the corresponding trend data; The risk rating results are processed by graph construction based on a preset graph construction strategy to obtain the corresponding graph data; The project record data, the risk rating results, the comprehensive risk level, the trend data, and the graph data are integrated and packaged to obtain the corresponding response data. The response data is rendered into the display area of ​​the target interface.

[0007] Secondly, a data processing apparatus is provided, comprising: A receiving module is used to receive data processing requests sent by users through a target interface; wherein the data processing request carries request parameters. The first processing module is used to extract the request parameters from the data processing request and query the project record data of the target project related to the request parameters from a preset database. The rating module is used to perform risk rating processing on the project record data based on a preset multi-dimensional risk rating strategy to obtain a risk rating result corresponding to the target project. The assessment module is used to comprehensively assess the risk rating results to obtain the corresponding comprehensive risk level. The analysis module is used to perform dynamic trend analysis on the comprehensive risk level based on the project record data, and obtain the corresponding trend data; The construction module is used to perform graph construction processing on the risk rating results based on a preset graph construction strategy to obtain the corresponding graph data; The second processing module is used to integrate and encapsulate the project record data, the risk rating results, the comprehensive risk level, the trend data, and the graph data to obtain the corresponding response data. The rendering module is used to render the response data to the display area in the target interface.

[0008] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described data processing method.

[0009] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described data processing method.

[0010] In the above-described data processing method, apparatus, computer equipment, and storage medium, the following steps are taken: First, a data processing request is received from a user through a target interface; the data processing request carries request parameters. Then, the request parameters are extracted from the data processing request, and project record data of the target project related to the request parameters is retrieved from a preset database. Next, the project record data is subjected to risk rating processing based on a preset multi-dimensional risk rating strategy to obtain a risk rating result corresponding to the target project. The risk rating result is then comprehensively evaluated to obtain a corresponding comprehensive risk level. Subsequently, the comprehensive risk level is dynamically analyzed based on the project record data to obtain corresponding trend data. The risk rating result is then subjected to graph construction processing based on a preset graph construction strategy to obtain corresponding graph data. Further, the project record data, the risk rating result, the comprehensive risk level, the trend data, and the graph data are integrated and encapsulated to obtain corresponding response data. Finally, the response data is rendered into the display area of ​​the target interface. Based on the above automated processing flow, this application provides an intelligent data processing method. By receiving data processing requests from users through the target interface and extracting request parameters, it retrieves project record data related to the request parameters from a preset database, achieving precise risk assessment and on-demand data acquisition. Then, based on a preset multi-dimensional risk rating strategy, the project record data is processed for risk rating, yielding a risk rating result. This overcomes the shortcomings of existing technologies that rely solely on a single indicator for simple grading, enabling multi-dimensional analysis of project risks and improving the comprehensiveness of risk assessment. Subsequently, the risk rating result is comprehensively evaluated to obtain a comprehensive risk level. Combined with dynamic trend analysis and graph construction, the project record data, risk rating result, comprehensive risk level, trend data, and graph data are integrated, encapsulated, and rendered onto the display area of ​​the target interface. This achieves multi-dimensional, dynamic, and visual display of risk information, solving the problems of limited risk display methods and low intelligence in existing systems. It effectively improves the intelligence of data display, enhances the refinement of project management, and helps managers make more accurate decisions. Attached Figure Description

[0011] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is an exemplary system architecture diagram to which this application can be applied; Figure 2 This is a flowchart of an embodiment of the data processing method according to this application; Figure 3 This is a schematic diagram of the structure of an embodiment of the data processing apparatus according to this application; Figure 4 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0013] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0014] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0015] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0016] like Figure 1 As shown, system architecture 100 may include terminal device 101, network 102, and server 103. Terminal device 101 may be a laptop 1011, tablet 1012, or mobile phone 1013. Network 102 is used as a medium to provide a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0017] Users can use terminal device 101 to interact with server 103 via network 102 to receive or send messages, etc. Various communication client applications can be installed on terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0018] Terminal device 101 can be various electronic devices with a display screen and support web browsing. In addition to laptops 1011, tablets 1012, or mobile phones 1013, terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer, and a desktop computer, etc.

[0019] Server 103 can be a server that provides various services, such as a backend server that provides support for the pages displayed on terminal device 101.

[0020] It should be noted that the data processing method provided in the embodiments of this application is generally executed by a server / terminal device, and correspondingly, the data processing device is generally located in the server / terminal device.

[0021] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0022] Continue to refer to Figure 2 A flowchart illustrating an embodiment of the data processing method according to this application is shown. The order of steps in the flowchart can be changed, and some steps can be omitted, depending on different needs. The data processing method provided in this application embodiment can be applied to any scenario requiring data processing, and thus can be applied to products in these scenarios, such as data processing products in the financial insurance and healthcare fields. The data processing method includes the following steps: Step S201: Receive a data processing request sent by the user through the target interface; wherein the data processing request carries request parameters.

[0023] In this embodiment, the data processing method runs on an electronic device (e.g., Figure 1The server / terminal device shown can acquire data processing requests issued by the user through the target interface via wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra-wideband) connections, and other currently known or future developed wireless connection methods. The executing entity of this application is specifically a data processing system, which may be simply referred to as the system. The aforementioned target interface can refer to a graphical user interface (GUI) where users can select or input filter criteria based on actual business needs, and initiate a corresponding data processing request by clicking the submit button. The data processing request carries request parameters corresponding to the filter criteria, including: project code (string, optional), task type (enumerated value, optional), task status (enumerated value, optional), year (integer, optional), sorting dimension (enumerated value, required, possible values ​​are "project level", "importance", "investment return", "operational status", "corporate governance", "compliance and risk control", "construction period and quality"), sorting direction (enumerated value, optional, possible values ​​are "ascending" and "descending"), current page number (integer, required), and number of items per page (integer, required).

[0024] This application can be applied to project analysis scenarios in the financial insurance and healthcare sectors. For example, in the financial insurance sector, the business scenario corresponding to the above data processing request could be: A life insurance company is developing a participating annuity insurance product. This product is in the actuarial modeling and bancassurance channel integration stage and has not yet been launched. The system needs to evaluate its potential investment return rate according to the investment return dimension and determine its priority in the product matrix. Correspondingly, the above request parameters could include: Project Code BX-2025-0037, Task Type = Insurance Product Development, Task Status = Under Development, Year = 2025, Sorting Dimension = Investment Return.

[0025] Alternatively, in the financial insurance sector, the business scenario corresponding to the above data processing request could be: A property insurance company has launched an intelligent claims system, using OCR and a rules engine to replace manual review of small-amount auto insurance claims. The project has been completed and accepted, and the system needs to retrospectively analyze its cost reduction and efficiency improvement effects based on operational status to decide whether to promote it to other insurance types. Correspondingly, the request parameters could include: Project Code BX-2024-0112, Task Type = Claims Process Improvement, Task Status = Launched, Year = 2024, Sorting Dimension = Operational Status.

[0026] Furthermore, in the healthcare field, the business scenario corresponding to the aforementioned data processing request could be as follows: A pharmaceutical company is advancing a Phase III clinical trial of a PD-1 inhibitor, currently in the enrollment phase. The system needs to prioritize the drug within the R&D pipeline according to its project level (combining clinical stage, indication market size, and competitive landscape) to assist in resource allocation decisions. Correspondingly, the request parameters could include: Project Code YL-2026-0089, Task Type = New Drug Clinical Trial, Task Status = In Progress, Year = 2026, Sorting Dimension = Project Level.

[0027] Alternatively, in the healthcare field, the business scenario corresponding to the above data processing request could be: A top-tier hospital initiates an electronic medical record system upgrade project, aiming to pass the Level 4 Class A interoperability assessment. The project has just completed its feasibility study, and the system needs to determine its ranking in the annual IT budget based on importance (involving hard indicators for the assessment and the hospital's overall business coverage). Correspondingly, the request parameters could include: Project Code YL-2025-0341, Task Type = Hospital Information System Upgrade, Task Status = Initiated, Year = 2025, Ranking Dimension = Importance.

[0028] Step S202: Extract the request parameters from the data processing request, and query the project record data of the target project related to the request parameters from the preset database.

[0029] In this embodiment, the request parameters carried in the data processing request can be extracted by parsing the data processing request. Furthermore, the specific implementation process of retrieving the project record data of the target project related to the request parameters from a preset database will be described in further detail in subsequent embodiments of this application, and will not be elaborated upon here.

[0030] Step S203: Perform risk rating processing on the project record data based on a preset multi-dimensional risk rating strategy to obtain the risk rating result corresponding to the target project.

[0031] In this embodiment, the specific implementation process of performing risk rating processing on the project record data based on the preset multi-dimensional risk rating strategy to obtain the risk rating result corresponding to the target project will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0032] Step S204: Perform a comprehensive evaluation of the risk rating results to obtain the corresponding comprehensive risk level.

[0033] In this embodiment, the specific implementation process of comprehensively evaluating the risk rating results to obtain the corresponding comprehensive risk level will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0034] Step S205: Based on the project record data, perform dynamic trend analysis on the comprehensive risk level to obtain the corresponding trend data.

[0035] In this embodiment, the specific implementation process of performing dynamic trend analysis on the comprehensive risk level based on the project record data to obtain the corresponding trend data will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0036] Step S206: Perform graph construction processing on the risk rating results based on a preset graph construction strategy to obtain the corresponding graph data.

[0037] In this embodiment, the specific implementation process of performing graph construction processing on the risk rating results based on the preset graph construction strategy to obtain the corresponding graph data will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0038] Step S207: Integrate and encapsulate the project record data, the risk rating results, the comprehensive risk level, the trend data, and the graph data to obtain the corresponding response data.

[0039] In this embodiment, the system aggregates the processed result data into a complete response data packet. The response data packet contains the following fields: an array of project lists (each project record includes the project code, project name, risk rating results across five dimensions, overall risk level, change arrow, and update time), graph data (containing five standardized values ​​for the current state and five standardized values ​​for the historical state), and project record data. Furthermore, the system encapsulates the data according to a unified JSON response structure to obtain the corresponding response data. The response data contains three top-level fields: code (status code, "SUCCESS" for success, "FAIL" for failure), message (error message, an empty string for success, and a detailed error description for failure), and data (data content object, containing all the above fields).

[0040] Step S208: Render the response data into the display area of ​​the target interface.

[0041] In this embodiment, after obtaining the encapsulated response data, the response data is transmitted to the target interface (i.e., the user graphical interface) on the front end. Upon receiving the response data, the user graphical interface renders it to the corresponding display area according to each subfield in the data field. Specifically, the item list is rendered to the left or central main area, with a change arrow and update time displayed on the right side of each item; the graph data is rendered to the central or upper five-dimensional graph display area; and the item record data is rendered to the data area on the right.

[0042] This application first receives a data processing request from a user through a target interface; wherein the data processing request carries request parameters; then, the request parameters are extracted from the data processing request, and project record data of the target project related to the request parameters are queried from a preset database; subsequently, the project record data is processed for risk rating based on a preset multi-dimensional risk rating strategy to obtain a risk rating result corresponding to the target project; and the risk rating result is comprehensively evaluated to obtain a corresponding comprehensive risk level; subsequently, the comprehensive risk level is dynamically analyzed based on the project record data to obtain corresponding trend data; and the risk rating result is processed for graph construction based on a preset graph construction strategy to obtain corresponding graph data; further, the project record data, the risk rating result, the comprehensive risk level, the trend data, and the graph data are integrated and encapsulated to obtain corresponding response data; finally, the response data is rendered in the display area of ​​the target interface. Based on the above automated processing flow, this application provides an intelligent data processing method. By receiving data processing requests from users through the target interface and extracting request parameters, it retrieves project record data related to the request parameters from a preset database, achieving precise risk assessment and on-demand data acquisition. Then, based on a preset multi-dimensional risk rating strategy, the project record data is processed for risk rating, yielding a risk rating result. This overcomes the shortcomings of existing technologies that rely solely on a single indicator for simple grading, enabling multi-dimensional analysis of project risks and improving the comprehensiveness of risk assessment. Subsequently, the risk rating result is comprehensively evaluated to obtain a comprehensive risk level. Combined with dynamic trend analysis and graph construction, the project record data, risk rating result, comprehensive risk level, trend data, and graph data are integrated, encapsulated, and rendered onto the display area of ​​the target interface. This achieves multi-dimensional, dynamic, and visual display of risk information, solving the problems of limited risk display methods and low intelligence in existing systems. It effectively improves the intelligence of data display, enhances the refinement of project management, and helps managers make more accurate decisions.

[0043] In some alternative implementations, step S202 includes the following steps: Step S2021: Perform a validity check on the request parameters based on a preset verification strategy.

[0044] In this embodiment, the process of validating the above request parameters includes: After receiving the request parameters, the system first performs format validation to determine whether the request parameters conform to the standard syntax structure. Then, it checks each required field (such as project code, task type, task status, sorting dimension, current page number, and number of items per page) to ensure they exist and are not empty. Next, it performs data type validation: the year must be a positive integer, the page number must be an integer greater than or equal to 1, the number of items per page must be an integer between 1 and 100, and the sorting dimension must be in a preset enumeration list. If the request parameters pass both format and data type validation, the request parameters are considered to have passed the validity check; otherwise, if either validation fails, the system directly returns a uniform error response, the response body of which includes the status code "FAIL", the error code field "INVALID_PARAMETER", and specific error description information.

[0045] Step S2022: If the request parameters pass the validity check, then the request parameters are decomposed to obtain the corresponding query condition set.

[0046] In this embodiment, after the legality verification passes, the system decomposes the above request parameters into two independent parameter subsets, from which the corresponding query condition sets are extracted. The first subset is the query condition set, which includes project code, task type, task status, and year. This query condition set is used to filter raw data that meets the conditions from the database. The second subset is the display control set, which includes sorting dimension, sorting direction, current page number, and number of items per page. This subset can be used for subsequent display control operations such as sorting and pagination of the query results.

[0047] Step S2023: Convert the set of query conditions into filtering conditions for a database query statement.

[0048] In this embodiment, the system converts the decomposed query condition set into filter conditions for the database query statement. Specifically, if the user specifies a project code, a filter condition is added where the project code equals the specified value; if a task type is specified, a filter condition is added where the task type equals the specified value; if a task status is specified, a filter condition is added where the task status equals the specified value; and if a year is specified, a filter condition is added where the task creation year equals the specified value. Multiple conditions are combined using a logical AND relationship to ensure that the returned data simultaneously meets all filtering requirements.

[0049] Step S2024: Perform a multi-table join query on the database based on the filtering conditions to obtain the corresponding query results.

[0050] In this embodiment, the system performs a joint query on the pre-built database's project basic information table, risk indicator data table, task record table, and historical risk rating table based on pre-defined filtering conditions. The query uses the project table as the main table, with the project code as the join key. It then left-joins the risk indicator data table (each project corresponds to multiple risk indicator records belonging to five different dimensions), the task record table (each project corresponds to multiple task records), and the historical risk rating table (each project corresponds to multiple historical rating records sorted by time), and returns the query results. Furthermore, if the query result is empty, the system returns a uniformly formatted empty data response with a status code of "SUCCESS" and an empty array as the data content.

[0051] Step S2025: Aggregate the query results based on a preset project aggregation strategy to obtain the corresponding aggregated data.

[0052] In this embodiment, after the query results are returned, the system uses the project code as the aggregation key to merge all related data of the same project into a single project record (i.e., aggregated data). Each project record includes: project code, project name, province, province code, return on investment, operating cash flow, revenue growth rate, cost control rate, governance structure score, management stability score, information disclosure quality score, number of compliance incidents, number of penalty records, risk control measure coverage rate, schedule deviation rate, quality pass rate, number of safety incidents, and the project's most recent risk rating record at a historical point in time (including the five dimensions of each rating, the overall rating, and the rating time).

[0053] Step S2026: Use the aggregated data as the project record data of the target project.

[0054] Based on the above processing flow, the data query and aggregation processing provided in this application integrates project-related data scattered across multiple tables into a complete dataset based on each project. Aggregation by project ensures that all dimensional indicators and historical records for each project are concentrated in a single record, which is beneficial for providing complete and accurate input data for subsequent multi-dimensional risk calculations.

[0055] In some optional implementations of this embodiment, step S203 includes the following steps: Step S2031: Perform risk rating processing on the project record data based on the preset investment return risk rating strategy to obtain the corresponding investment return risk level.

[0056] In this embodiment, the specific implementation process of performing risk rating processing on the project record data based on the preset investment return risk rating strategy to obtain the corresponding investment return risk level will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0057] Step S2032: Perform risk rating processing on the project record data based on the preset operational risk rating strategy to obtain the corresponding operational risk level.

[0058] In this embodiment, the system extracts three operational indicators—operating cash flow, revenue growth rate, and cost control rate—from the aggregated project record data. Threshold ranges are defined: positive operating cash flow with a revenue growth rate ≥ 10% and a cost control rate ≥ 90% corresponds to "low risk"; positive operating cash flow with (revenue growth rate ≥ 5% or cost control rate ≥ 85%) corresponds to "low-to-medium risk"; positive operating cash flow with (revenue growth rate ≥ 0% or cost control rate ≥ 80%) corresponds to "medium risk"; positive operating cash flow with revenue growth rate < 0% or cost control rate < 80% corresponds to "medium-to-high risk"; and negative operating cash flow corresponds to "high risk." The system then compares each operational indicator with its corresponding threshold range, determining the risk level based on which range it falls into. Finally, using the same majority voting and conservative principles as the investment return dimension, a comprehensive analysis of the risk levels of all operational indicators is conducted to determine the final operational risk level.

[0059] Step S2033: Perform risk rating processing on the project record data based on the preset governance risk rating strategy to obtain the corresponding governance risk level.

[0060] In this embodiment, the system extracts three governance indicators from the aggregated project record data: governance structure score (0-100 points), management stability score (0-100 points), and information disclosure quality score (0-100 points). Thresholds are set: all three scores ≥ 80 points correspond to "low risk"; at least two scores ≥ 80 points and none below 60 points correspond to "low-to-medium risk"; at least two scores ≥ 60 points correspond to "medium risk"; at least one score < 60 points correspond to "high-to-medium risk"; and any score < 40 points correspond to "high risk". The system then compares each governance indicator with its corresponding threshold range, determining the risk level based on which range it falls into. Finally, using the same majority voting and conservative principles as the investment return dimension, a comprehensive analysis of the risk levels of all governance indicators is conducted to determine the final governance risk level.

[0061] It also employs majority voting and the principle of conservatism.

[0062] Step S2034: Perform risk rating processing on the project record data based on the preset compliance risk rating strategy to obtain the corresponding compliance risk level.

[0063] In this embodiment, the system extracts three compliance indicators from the aggregated project record data: the number of compliance events, the number of penalty records, and the coverage rate of risk control measures. Thresholds are set as follows: 0 compliance events, 0 penalty records, and a risk control measure coverage rate ≥ 95% correspond to "low risk"; ≤ 1 compliance events, 0 penalty records, and a risk control measure coverage rate ≥ 90% correspond to "low to medium risk"; ≤ 3 compliance events, ≤ 1 penalty records, and a risk control measure coverage rate ≥ 85% correspond to "medium risk"; ≤ 5 compliance events, ≤ 2 penalty records, and a risk control measure coverage rate ≥ 80% correspond to "medium to high risk"; and > 5 compliance events, > 2 penalty records, or < 80% risk control measure coverage rate correspond to "high risk". The system then compares each governance indicator with its corresponding threshold range, determining the risk level based on which range it falls into. Finally, using the same majority voting and conservative principles as the investment return dimension, a comprehensive analysis of the risk levels of all compliance indicators is conducted to determine the final compliance risk level.

[0064] Step S2035: Perform risk rating processing on the project record data based on the preset quality risk rating strategy to obtain the corresponding quality risk level.

[0065] In this embodiment, the system extracts three quality indicators from the aggregated project record data: schedule deviation rate, quality pass rate, and number of safety incidents. Thresholds are set: schedule deviation rate ≤ 5%, quality pass rate ≥ 98%, and number of safety incidents = 0 corresponds to "low risk"; schedule deviation rate ≤ 10%, quality pass rate ≥ 95%, and number of safety incidents ≤ 1 corresponds to "low to medium risk"; schedule deviation rate ≤ 15%, quality pass rate ≥ 90%, and number of safety incidents ≤ 2 corresponds to "medium risk"; schedule deviation rate ≤ 20%, quality pass rate ≥ 85%, and number of safety incidents ≤ 3 corresponds to "medium to high risk"; schedule deviation rate > 20%, quality pass rate < 85%, or number of safety incidents > 3 corresponds to "high risk". Then, the system compares each quality indicator with its corresponding threshold range, and the range it falls into determines the risk level of that range. Furthermore, using the same majority voting and conservative principles as the investment return dimension, a comprehensive analysis of the risk levels of all quality indicators is conducted to determine the final quality risk level.

[0066] Step S2036: Integrate the investment return risk level, the operational risk level, the governance risk level, the compliance risk level, and the quality risk level to obtain the corresponding first integrated data.

[0067] In this embodiment, the generated investment return risk level, operational risk level, governance risk level, compliance risk level, and quality risk level can be integrated and processed, and the resulting first integrated data can be used as the risk rating result of the target project.

[0068] Step S2037: Use the first integrated data as the risk rating result of the target project.

[0069] Based on the above processing flow, the multi-dimensional risk rating process provided in this application is the core component of the entire system. The five dimensions are calculated independently. Within each dimension, a risk level is determined through threshold comparison and a voting mechanism based on multiple indicators. A conservative principle is adopted (the higher risk level is used when there is disagreement among indicators) to ensure that project risks are not underestimated. Ultimately, each dimension outputs a five-level risk rating (low / low-medium / medium / high-medium / high), which is beneficial for providing an accurate data foundation for subsequent comprehensive risk assessment and dynamic trend analysis.

[0070] In some alternative implementations, step S2031 includes the following steps: Step S20311: Extract investment indicators corresponding to preset indicator types from the project record data; wherein, the number of investment indicators is multiple.

[0071] In this embodiment, the aforementioned indicator types include three types: Return on Investment (ROI), Net Present Value (NPV), and Internal Rate of Return (IRR). Based on these indicator types, the system can extract the corresponding three investment indicators—ROI, NPV, and IRR—from the aggregated project record data.

[0072] Step S20312: Based on preset indicator thresholds, each of the investment indicators is compared and processed, and based on the comparison results, a risk level corresponding to each of the investment indicators is generated.

[0073] In this embodiment, a five-level threshold range is pre-defined for each investment indicator. Taking the rate of return on investment (ROI) as an example, the thresholds are: ROI ≥ 8% corresponds to "low risk", 5% ≤ ROI < 8% corresponds to "low to medium risk", 2% ≤ ROI < 5% corresponds to "medium risk", 0% ≤ ROI < 2% corresponds to "high to medium risk", and ROI < 0% corresponds to "high risk". The system then compares the project's actual ROI value with each threshold range. The risk level of the investment indicator is determined by the threshold range it falls into. For example, if the project's actual ROI value falls into the 5% ≤ ROI < 8% threshold range, the risk level of the ROI is "low to medium risk".

[0074] Step S20313: Based on the preset majority voting and conservative principles, a comprehensive analysis of all the risk levels is performed to generate the corresponding target risk level.

[0075] In this embodiment, a comprehensive analysis of all the aforementioned risk levels can be conducted using majority voting and a conservative approach to determine the final risk level corresponding to the investment return dimension, i.e., the investment return risk level. Specifically, if at least two of the three investment indicators point to the same risk level, the majority voting principle is adopted, meaning the risk level that appears most frequently among the three investment indicators is taken as the final risk level for that investment return dimension. If the risk assessment results of the three investment indicators are different, the one with the highest risk level is taken as the final level to ensure the conservatism of the risk assessment.

[0076] Step S20314: The target risk level is used as the investment return risk level.

[0077] Based on the above processing flow, within the investment return dimension, this application uses threshold comparison and voting mechanisms to derive risk levels for multiple investment indicators, and adopts a conservative principle (taking the higher risk level when there is disagreement among indicators) to ensure that project risks are not underestimated, effectively ensuring the accuracy and intelligence of the generated investment return risk levels.

[0078] In some alternative implementations, step S204 includes the following steps: Step S2041: Perform numerical processing on the risk rating result to obtain the corresponding numerical rating.

[0079] In this embodiment, in order to perform weighted calculation, the system maps the risk levels (low / low-medium / medium / high-medium / high) of the five dimensions included in the above risk rating results to the corresponding numerical levels: low = 1, low-medium = 2, medium = 3, high-medium = 4, high = 5.

[0080] Step S2042: Obtain the preset risk weights.

[0081] In this embodiment, based on actual business needs, the system presets default weight values ​​(i.e., risk weights) for five dimensions: Investment Returns W1 = 0.25, Operational Performance W2 = 0.20, Corporate Governance W3 = 0.15, Compliance and Risk Control W4 = 0.25, and Construction Period and Quality W5 = 0.15. The sum of the five weights equals 1, i.e., W1 + W2 + W3 + W4 + W5 = 1. Administrators can configure and adjust these weight values ​​in the backend; adjustments take effect immediately.

[0082] Step S2043: Calculate the numerical level and the risk weight based on the preset comprehensive level calculation formula to obtain the corresponding calculation result.

[0083] In this embodiment, the comprehensive risk value is calculated according to the comprehensive level calculation formula: R_Comprehensive = W1×R1+ W2×R2+ W3×R3+ W4×R4+ W5×R5 (where R1 to R5 are the numerical levels of the five dimensions, and W1 to W5 are the corresponding weights), which is the calculation result above.

[0084] Step S2044: Determine the corresponding comprehensive risk level based on the calculation results.

[0085] In this embodiment, the corresponding comprehensive risk level is determined based on the range of the comprehensive risk value: R_comprehensive < 1.8 corresponds to "low risk", 1.8 ≤ R_comprehensive < 2.5 corresponds to "low to medium risk", 2.5 ≤ R_comprehensive < 3.5 corresponds to "medium risk", 3.5 ≤ R_comprehensive < 4.2 corresponds to "medium to high risk", and R_comprehensive ≥ 4.2 corresponds to "high risk".

[0086] In addition, the system further has a portrait construction function. Specifically, the system assembles the following information into a complete project risk portrait record: project code, project name, affiliated province, affiliated province code, investment return dimension level and its original indicator value, operation status dimension level and its original indicator value, corporate governance dimension level and its original indicator value, compliance and risk control dimension level and its original indicator value, construction period and quality dimension level and its original indicator value, comprehensive risk level, comprehensive risk value, latest rating update time, and previous comprehensive risk level. The generated project risk portrait record can be used as a data source for all subsequent presentation modules.

[0087] Based on the above processing flow, the present application fuses the numerical levels of multiple dimensions into a single comprehensive numerical value by using a comprehensive level calculation formula, and then converts it into an intuitive risk level through interval mapping, thereby automatically and accurately integrating the risk assessment results of multiple independent dimensions into an overall comprehensive risk level, ensuring the accuracy and intelligence of the generated comprehensive risk level.

[0088] In some optional implementation manners of this embodiment, step S205 includes the following steps: Step S2051, extracting historical rating results from the project record data.

[0089] In this embodiment, the historical risk rating record of the target project can be obtained from the above project record data, sorted in reverse chronological order of rating, and the latest historical rating result is extracted. The historical rating result is recorded as R_previous, which includes its five-dimensional levels and comprehensive level; and the current rating result is recorded as R_current, that is, the newly calculated comprehensive risk level.

[0090] Step S2052, performing change direction recognition processing on the historical rating result and the comprehensive risk level to obtain a corresponding change direction.

[0091] In this embodiment, R_current (comprehensive risk level) is compared with R_previous (historical rating result). If the value of R_current is greater than the value of R_previous (that is, the comprehensive risk level rises), the change direction is marked as ↑; if the value of R_current is less than the value of R_previous (that is, the comprehensive risk level falls), the change direction is marked as ↓; if the value of R_current is equal to the value of R_previous (that is, the comprehensive risk level remains unchanged), the change direction is marked as —.

[0092] Step S2053, performing change cause analysis processing on the comprehensive risk level based on the historical rating result to obtain a corresponding change cause.

[0093] In this embodiment, the system simultaneously extracts the main dimensions that caused the change in the overall rating from the current round of risk rating calculation. By comparing the rating differences between R_when and R_on across the five dimensions, the system identifies the dimension with the largest increase in risk level among the changed dimensions and uses it as the main reason for the change. For example, if the investment return dimension changes from "medium" to "medium-high" while other dimensions remain unchanged, the reason for the change is "the risk level of the investment return dimension has been adjusted upwards." If multiple dimensions change simultaneously, the dimension with the highest weight is selected as the main reason.

[0094] Step S2054: Obtain the timestamp of the completion of the comprehensive risk level calculation.

[0095] In this embodiment, the system will pre-record the time when the risk rating calculation is completed, accurate to the second, in the format "YYYY-MM-DD HH:MM:SS", as the update timestamp.

[0096] Step S2055: Integrate the change direction, the cause of change, and the timestamp to obtain the corresponding second integrated data.

[0097] In this embodiment, the obtained change direction, change cause and timestamp can be integrated, and the generated second integrated data can be used as the corresponding trend data.

[0098] Step S2056: Use the second integrated data as the trend data.

[0099] Based on the above processing flow, this application uses project record data to dynamically analyze the changing trends of the comprehensive risk level, allowing users to easily see whether the project risk is improving or worsening. Furthermore, by comparing the two most recent rating results, the direction of change is visually displayed using arrow symbols, while simultaneously recording the reasons for the change and a timestamp, enabling users not only to know that "it has changed," but also "why it changed" and "when it changed." This design makes the evolution of risk traceable and understandable, thus improving the intelligence of data presentation.

[0100] In some optional implementations of this embodiment, step S206 includes the following steps: Step S2061: Perform numerical standardization on the risk rating result to obtain the corresponding standardized value.

[0101] In this embodiment, the numerical levels (1 to 5) of the five dimensions contained in the risk rating results are first extracted, and then linearly mapped to a standard range of 0 to 100. The standardization formula used is: V_standardization = (R_dimension - 1) / (5 - 1) × 100 (where R_dimension is the numerical level of the dimension, with a value range of 1 to 5). For example, if the investment return dimension level is "medium-high" (value is 4), then V_investment return = (4 - 1) / 4 × 100 = 75.

[0102] Step S2062: Obtain the historical standardized value generated by the previous rating process corresponding to the target project.

[0103] In this embodiment, the system simultaneously extracts the numerical levels of the five dimensions of the target project's previous rating, calculates the five historical standardized values ​​of the previous rating using the same standardized formula, and uses these as historical state data points. These five points are connected by dashed lines to form the previous pentagon.

[0104] Step S2063: Call the preset initial graphic.

[0105] In this embodiment, the initial graphic is a pre-created five-dimensional graphic.

[0106] Step S2064: Based on a preset drawing mechanism, the standardized value and the historical standardized value are drawn to the corresponding positions within the initial graphic to obtain the corresponding target graphic.

[0107] In this embodiment, the standardized values ​​of the five dimensions are used as data points on the five axes of a five-dimensional graph, which are then connected sequentially to form a closed pentagon. The shape of this pentagon intuitively reflects the risk distribution of the project across each dimension. The closer the pentagon is to the center, the lower the overall risk of the project; the further outward it expands, the higher the risk in the corresponding dimension. Simultaneously, the five historical standardized values ​​are used as historical state data points, and these five points are connected by dashed lines to form the previous pentagon. Furthermore, when the system simultaneously draws the current state solid-line pentagon and the historical state dashed-line pentagon on the five-dimensional graph, for each dimension, the current standardized value is compared with the historical standardized value: if the current value is greater than the historical value, it indicates that the risk of that dimension has increased, and this is marked with a red arrow on the axis; if the current value is less than the historical value, it indicates that the risk of that dimension has decreased, and this is marked with a green arrow on the axis; if they are equal, no marking is used. This process allows the standardized values ​​and historical standardized values ​​to be drawn to their corresponding positions within the initial graph, and the resulting target graph is used as the corresponding graph data.

[0108] Step S2065: Use the target graphic as the graphic data.

[0109] Based on the above processing flow, this application uses a graph construction strategy to process risk rating results into graphs, automatically and intelligently converting risk levels across multiple dimensions into graphical representations. Through standardization, data from different dimensions can be plotted on the same scale. It simultaneously displays the current and historical states, and indicates the direction of change, allowing users to intuitively see the risk distribution and trends across various dimensions of the project without having to read numbers, significantly improving information retrieval efficiency.

[0110] In some optional implementations, the system also has the function of regionally distributed data processing. The specific implementation process is as follows: 1) Province Grouping: The system groups all project records from the database query results according to their respective province codes. Each province code corresponds to a project group, which contains all projects under that province. 2) Risk Level Statistics: For each province group, the system counts the number of projects at each risk level within that group. Specifically, it uses five counters: N_Low (number of low-risk projects), N_Low-Medium (number of low-medium-risk projects), N_Medium (number of medium-risk projects), N_High-Medium (number of high-medium-risk projects), and N_High (number of high-risk projects). It also calculates the total number of risk events for that province, which is the sum of the number of compliance events for all projects in that province plus the sum of the number of safety incidents. 3) Province Code Association: The system associates the statistical results with the province codes to form a regional statistical record, including: province code, province name, number of low-risk projects, number of low-medium-risk projects, number of medium-risk projects, number of high-medium-risk projects, number of high-risk projects, and total number of risk events. 4) Map Data Conversion: The system converts the regional statistical records into the data format required by the map visualization module. For each province, a risk density value is calculated based on the ratio of high-risk projects to the total number of projects. The higher the risk density value, the darker the color or the denser the markings on the map. Simultaneously, the total number of risk events is used as annotation information for the province, displayed in a floating tooltip on the map.

[0111] Regional distribution data processing involves converting project-based data into aggregated statistical data at the provincial level. Through grouped statistics and risk density calculations, users can quickly identify which provinces have a high concentration of high-risk projects on the map, thus aiding resource allocation and risk management decisions. Furthermore, the map statistical data can be rendered to the right or top map visualization area of ​​the target interface, enabling the right-side statistical panel to interact with the map and providing accurate numerical support.

[0112] In some optional implementations, the system also has the function of task data association and tracking, and the specific implementation process includes: 1) Task Query: The system queries the task record table for matching task records based on three conditions selected by the user: project code, task type, and task status. Query conditions are combined using logical AND relationships. If the user does not specify a condition, that condition is not included in the filtering. 2) Task Information Assembly: For each retrieved task record, the system assembles the following information: task code, associated project code, task type, current status (pending / processing / completed), creation time (format "YYYY-MM-DD HH:MM:SS"), and latest feedback information (if any). For tasks with a status of "processing" or "completed," the handler information and completion time (if completed) are additionally extracted. 3) Redirect Link Generation: The system generates a redirect link for each task record. The link address is the URL of the task details page, and the URL carries the task code as a parameter, in the format " / task / detail?taskId=task code". After the user clicks the link, the front end redirects to the corresponding task details page, displaying the complete information of the task. 4) Year Enumeration Extraction: The system queries the project calendar table for all existing year records, extracts unique year values, and sorts them in ascending order to form a year enumeration list. For example, if the project calendar contains task records for 2023, 2024, 2025, and 2026, the year enumeration list will be [2023, 2024, 2025, 2026]. This list is returned to the front end, allowing users to filter tasks by year on the graphical user interface.

[0113] The task data association and tracking section links project risk profiles with specific tasks. Through multi-condition queries and jump links, users can directly delve into the execution details of specific tasks from the project risk overview. The provision of year enumeration allows users to quickly locate tasks by time, improving the flexibility and efficiency of task management. Additionally, the year enumeration can be rendered into the year dropdown menu in the filter bar.

[0114] In some optional implementations, the system also has sorting, filtering, and pagination functions, the specific implementation process of which includes: 1) Sorting Execution: The system sorts the project list according to the sorting dimension selected by the user. If the sorting dimension is "Project Level", it is arranged in descending order or ascending order of comprehensive risk level; if the sorting dimension is "Investment Return", it is arranged in descending order or ascending order of investment return rate (because the lower the return rate, the higher the risk); if the sorting dimension is "Operating Status", it is arranged in descending order of risk level according to the operating status dimension; other dimensions are similar. The sorting uses a stable sorting algorithm to ensure that projects of the same level maintain their original relative order. 2) Filtering Execution: The system applies the filtering conditions specified by the user. If the user specifies a risk level filter (e.g., only displaying "High Risk" projects), projects with mismatched comprehensive levels are filtered out; if a year filter is specified, projects with mismatched task creation years are filtered out; if a task status filter is specified, projects with mismatched task statuses are filtered out. Filtering conditions and sorting conditions can be used together, with filtering performed before sorting. 3) Pagination Truncation: After sorting and filtering, the system calculates the starting and ending indices for data truncation based on the user-specified current page number P and the number of records per page S. Starting index = (P - 1) × S, Ending index = P × S - 1. The system truncates records within the index range from the sorted list of items, returning this data as the current page's data. Simultaneously, the system calculates the total number of records T, used for displaying pagination information such as "Total T records, current page P, S records per page" on the front end. If P is greater than the maximum page number, an empty array is returned with the message "Data range exceeded".

[0115] Sorting, filtering, and pagination are the data organization steps geared towards user interaction. Sorting allows users to quickly find target items based on their interests; filtering allows users to focus on a subset of items based on specific conditions; and pagination solves the performance and interface congestion problems caused by loading large amounts of data at once. Using these three functions together ensures that users can efficiently locate the information they need even amidst massive amounts of data.

[0116] In some alternative implementations, the user information obtained is subject to user consent and complies with relevant laws and policies.

[0117] Furthermore, any software tools or components not belonging to our company that appear in the embodiments of this application are merely illustrative examples and do not represent actual use.

[0118] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0119] It should be emphasized that, to further ensure the privacy and security of the above response data, the response data can also be stored in a node of a blockchain.

[0120] The blockchain referred to in this application is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0121] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0122] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0123] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0124] Further reference Figure 3 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of a data processing apparatus, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0125] like Figure 3 As shown, the data processing device 300 described in this embodiment includes: a receiving module 301, a first processing module 302, a rating module 303, an evaluation module 304, an analysis module 305, a construction module 306, a second processing module 307, and a rendering module 308. Wherein: The receiving module 301 is used to receive a data processing request sent by the user through the target interface; wherein the data processing request carries request parameters; The first processing module 302 is used to extract the request parameters from the data processing request and query the project record data of the target project related to the request parameters from a preset database. The rating module 303 is used to perform risk rating processing on the project record data based on a preset multi-dimensional risk rating strategy to obtain a risk rating result corresponding to the target project. The assessment module 304 is used to perform a comprehensive assessment of the risk rating results to obtain the corresponding comprehensive risk level. Analysis module 305 is used to perform dynamic trend analysis on the comprehensive risk level based on the project record data to obtain corresponding trend data; The construction module 306 is used to perform graph construction processing on the risk rating results based on a preset graph construction strategy to obtain the corresponding graph data; The second processing module 307 is used to integrate and encapsulate the project record data, the risk rating result, the comprehensive risk level, the trend data, and the graph data to obtain the corresponding response data. The rendering module 308 is used to render the response data to the display area in the target interface.

[0126] In some optional implementations of this embodiment, the first processing module 302 includes: The verification submodule is used to perform legality verification on the request parameters based on a preset verification strategy. The parsing submodule is used to decompose the request parameters to obtain the corresponding query condition set if the request parameters pass the validity check. The conversion submodule is used to convert the set of query conditions into filter conditions for a database query statement; The query submodule is used to perform multi-table join queries on the database based on the filtering conditions to obtain the corresponding query results. The aggregation submodule is used to aggregate the query results based on a preset project aggregation strategy to obtain the corresponding aggregated data. The first determining submodule is used to use the aggregated data as the project record data of the target project.

[0127] In some optional implementations of this embodiment, the rating module 303 includes: The first processing submodule is used to perform risk rating processing on the project record data based on a preset investment return risk rating strategy to obtain the corresponding investment return risk level. The second processing submodule is used to perform risk rating processing on the project record data based on a preset operational risk rating strategy to obtain the corresponding operational risk level. The third processing submodule is used to perform risk rating processing on the project record data based on a preset governance risk rating strategy to obtain the corresponding governance risk level. The fourth processing submodule is used to perform risk rating processing on the project record data based on a preset compliance risk rating strategy to obtain the corresponding compliance risk level. The fifth processing submodule is used to perform risk rating processing on the project record data based on a preset quality risk rating strategy to obtain the corresponding quality risk level. The first integration submodule is used to integrate the investment return risk level, the operational risk level, the governance risk level, the compliance risk level, and the quality risk level to obtain the corresponding first integrated data. The second determining submodule is used to use the first integrated data as the risk rating result of the target project.

[0128] In some optional implementations of this embodiment, the first processing submodule includes: An extraction unit is used to extract investment indicators corresponding to preset indicator types from the project record data; wherein, the number of investment indicators is multiple; The generation unit is used to compare each of the investment indicators based on a preset indicator threshold, and generate a risk level corresponding to each of the investment indicators based on the comparison results. The analysis unit is used to perform a comprehensive analysis of all the risk levels based on a preset majority vote and conservative principle, and generate a corresponding target risk level. A determining unit is used to determine the target risk level as the investment return risk level.

[0129] In some optional implementations of this embodiment, the evaluation module 304 includes: The numericalization submodule is used to perform numericalization processing on the risk rating results to obtain the corresponding numerical rating. The first acquisition submodule is used to acquire preset risk weights; The calculation submodule is used to calculate the numerical level and the risk weight based on a preset comprehensive level calculation formula to obtain the corresponding calculation results; The third determination submodule is used to determine the corresponding comprehensive risk level based on the calculation results.

[0130] In some optional implementations of this embodiment, the analysis module 305 includes: The extraction submodule is used to extract historical rating results from the project record data; The identification submodule is used to identify the direction of change of the historical rating results and the comprehensive risk level to obtain the corresponding direction of change. The analysis submodule is used to analyze the reasons for changes in the comprehensive risk level based on the historical rating results, and to obtain the corresponding reasons for the changes. The second acquisition submodule is used to acquire the timestamp of the completion of the comprehensive risk level calculation; The second integration submodule is used to integrate the change direction, the change reason and the timestamp to obtain the corresponding second integrated data. The fourth determining submodule is used to use the second integrated data as the trend data.

[0131] In some optional implementations of this embodiment, the construction module 306 includes: The standardization submodule is used to perform numerical standardization on the risk rating results to obtain the corresponding standardized values. The third acquisition submodule is used to acquire the historical standardized value generated by the previous rating process corresponding to the target project; Call the submodule to invoke the preset initial graphics; The drawing submodule is used to draw the standardized value and the historical standardized value to the corresponding positions within the initial graphic based on a preset drawing mechanism, so as to obtain the corresponding target graphic; The fifth determining submodule is used to use the target graphic as the graphic data.

[0132] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0133] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0134] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0135] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for data processing methods. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0136] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, for example, to execute computer-readable instructions for the data processing method.

[0137] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0138] Compared with the prior art, the embodiments of this application have the following beneficial effects: In this embodiment, an intelligent data processing method is provided. By receiving data processing requests from users through a target interface and extracting request parameters, the method retrieves project record data related to the request parameters from a preset database, achieving precise risk assessment and on-demand data acquisition. Then, based on a preset multi-dimensional risk rating strategy, the project record data is processed for risk rating, yielding a risk rating result. This overcomes the shortcomings of existing technologies that rely solely on a single indicator for simple grading, enabling multi-dimensional analysis of project risks and improving the comprehensiveness of risk assessment. Subsequently, the risk rating result is comprehensively evaluated to obtain a comprehensive risk level. Combined with dynamic trend analysis and graph construction, the project record data, risk rating result, comprehensive risk level, trend data, and graph data are integrated, encapsulated, and rendered onto the display area of ​​the target interface. This achieves multi-dimensional, dynamic, and visual display of risk information, solving the problems of limited risk display methods and low intelligence in existing systems. It effectively improves the intelligence of data display, enhances the refinement of project management, and helps managers make more accurate decisions.

[0139] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the data processing method described above.

[0140] Compared with the prior art, the embodiments of this application have the following main advantages: In this embodiment, an intelligent data processing method is provided. By receiving data processing requests from users through a target interface and extracting request parameters, the method retrieves project record data related to the request parameters from a preset database, achieving precise risk assessment and on-demand data acquisition. Then, based on a preset multi-dimensional risk rating strategy, the project record data is processed for risk rating, yielding a risk rating result. This overcomes the shortcomings of existing technologies that rely solely on a single indicator for simple grading, enabling multi-dimensional analysis of project risks and improving the comprehensiveness of risk assessment. Subsequently, the risk rating result is comprehensively evaluated to obtain a comprehensive risk level. Combined with dynamic trend analysis and graph construction, the project record data, risk rating result, comprehensive risk level, trend data, and graph data are integrated, encapsulated, and rendered onto the display area of ​​the target interface. This achieves multi-dimensional, dynamic, and visual display of risk information, solving the problems of limited risk display methods and low intelligence in existing systems. It effectively improves the intelligence of data display, enhances the refinement of project management, and helps managers make more accurate decisions.

[0141] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0142] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

[0143] It should be noted that any AI models, software tools, or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with the knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

Claims

1. A data processing method, characterized in that, Includes the following steps: Receive data processing requests sent by users through the target interface; wherein, the data processing requests carry request parameters; Extract the request parameters from the data processing request, and query the project record data of the target project related to the request parameters from the preset database; Based on a preset multi-dimensional risk rating strategy, the project record data is processed for risk rating to obtain a risk rating result corresponding to the target project. The risk rating results are comprehensively evaluated to obtain the corresponding comprehensive risk level; Based on the project record data, a dynamic trend analysis of the comprehensive risk level is performed to obtain the corresponding trend data; The risk rating results are processed by graph construction based on a preset graph construction strategy to obtain the corresponding graph data; The project record data, the risk rating results, the comprehensive risk level, the trend data, and the graph data are integrated and packaged to obtain the corresponding response data. The response data is rendered into the display area of ​​the target interface.

2. The data processing method according to claim 1, characterized in that, The step of retrieving project record data of the target project related to the request parameters from a preset database specifically includes: The validity of the request parameters is verified based on a preset verification strategy. If the request parameters pass the validity check, then the request parameters are decomposed to obtain the corresponding query condition set; The query condition set is converted into the filtering conditions of a database query statement; Based on the filtering conditions, perform a multi-table join query on the database to obtain the corresponding query results; The query results are aggregated based on a preset project aggregation strategy to obtain the corresponding aggregated data. The aggregated data is used as the project record data for the target project.

3. The data processing method according to claim 1, characterized in that, The step of performing risk rating processing on the project record data based on a preset multi-dimensional risk rating strategy to obtain a risk rating result corresponding to the target project specifically includes: The project record data is processed for risk rating based on a preset investment return risk rating strategy to obtain the corresponding investment return risk level. The project record data is processed for risk rating based on a preset operational risk rating strategy to obtain the corresponding operational risk level. The project record data is processed for risk rating based on a preset governance risk rating strategy to obtain the corresponding governance risk level. The project record data is processed for risk rating based on a preset compliance risk rating strategy to obtain the corresponding compliance risk level. The project record data is processed for risk rating based on a preset quality risk rating strategy to obtain the corresponding quality risk level. The investment return risk level, the operational risk level, the governance risk level, the compliance risk level, and the quality risk level are integrated to obtain the corresponding first integrated data; The first integrated data is used as the risk rating result for the target project.

4. The data processing method according to claim 3, characterized in that, The step of performing risk rating processing on the project record data based on a preset investment return risk rating strategy to obtain the corresponding investment return risk level specifically includes: Investment indicators corresponding to preset indicator types are extracted from the project record data; wherein, the number of investment indicators is multiple. Each investment indicator is compared based on a preset threshold, and a risk level corresponding to each investment indicator is generated based on the comparison results. Based on a preset majority vote and conservative principle, a comprehensive analysis of all the risk levels is performed to generate the corresponding target risk level. The target risk level is used as the investment return risk level.

5. The data processing method according to claim 1, characterized in that, The step of comprehensively evaluating the risk rating results to obtain the corresponding comprehensive risk level specifically includes: The risk rating results are processed into numerical levels to obtain the corresponding numerical levels; Obtain the preset risk weights; The numerical level and the risk weight are calculated based on the preset comprehensive level calculation formula to obtain the corresponding calculation results. The corresponding comprehensive risk level is determined based on the calculation results.

6. The data processing method according to claim 1, characterized in that, The step of performing dynamic trend analysis on the comprehensive risk level based on the project record data to obtain the corresponding trend data specifically includes: Historical rating results are extracted from the project record data; The historical rating results and the comprehensive risk level are processed to identify the direction of change, thereby obtaining the corresponding direction of change. Based on the historical rating results, the reasons for the changes in the comprehensive risk level are analyzed to obtain the corresponding reasons for the changes. Obtain the timestamp when the comprehensive risk level calculation is completed; The change direction, the cause of the change, and the timestamp are integrated to obtain the corresponding second integrated data; The second integrated data is used as the trend data.

7. The data processing method according to claim 1, characterized in that, The step of performing graph construction processing on the risk rating results based on a preset graph construction strategy to obtain the corresponding graph data specifically includes: The risk rating results are numerically standardized to obtain the corresponding standardized values. Obtain the historical standardized value generated from the previous rating process corresponding to the target project; Calls the preset initial graphic; Based on a preset drawing mechanism, the standardized value and the historical standardized value are drawn to the corresponding positions within the initial graphic to obtain the corresponding target graphic; The target graphic is used as the graphic data.

8. A data processing apparatus, characterized in that, include: A receiving module is used to receive data processing requests sent by users through a target interface; wherein the data processing request carries request parameters. The first processing module is used to extract the request parameters from the data processing request and query the project record data of the target project related to the request parameters from a preset database. The rating module is used to perform risk rating processing on the project record data based on a preset multi-dimensional risk rating strategy to obtain a risk rating result corresponding to the target project. The assessment module is used to comprehensively assess the risk rating results to obtain the corresponding comprehensive risk level. The analysis module is used to perform dynamic trend analysis on the comprehensive risk level based on the project record data, and obtain the corresponding trend data; The construction module is used to perform graph construction processing on the risk rating results based on a preset graph construction strategy to obtain the corresponding graph data; The second processing module is used to integrate and encapsulate the project record data, the risk rating results, the comprehensive risk level, the trend data, and the graph data to obtain the corresponding response data. The rendering module is used to render the response data to the display area in the target interface.

9. A computer device, characterized in that, The method includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the data processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the data processing method as described in any one of claims 1 to 7.

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